Book Image

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
Book Image

Scalable Data Analytics with Azure Data Explorer

By: Jason Myerscough

Overview of this book

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters. The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance. By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.
Table of Contents (18 chapters)
Section 1: Introduction to Azure Data Explorer
Section 2: Querying and Visualizing Your Data
Section 3: Advanced Azure Data Explorer Topics

Calculating statistics for time series data

Another useful function is series_stats(). The series_stats() function takes one or multiple time series as an argument and returns the following statistical information:

  • min: The minimum value in the time series.
  • min_idx: The index of the minimum value in the time series.
  • max: The maximum value in the time series.
  • max_idx: The index of the maximum value in the time series.
  • avg: The average value of the time series.
  • variance: The sample variance of the time series. The sample variance is the squared deviation of the time series's mean. The sample variance is used to calculate the standard deviation.
  • stdev: The sample standard deviation. The standard deviation is the amount of variation in the values of the time series.

The following screenshot shows the statistics for our security patching time series:

Figure 6.11 – Calculating statistics for a time series

As you...